What Is AI Engineering?

AI engineering bridges the gap between machine learning research and production systems. This track takes you from understanding generative AI and large language models through building RAG applications to deploying agentic AI systems with observability.

If you are new to the field, start with Introduction for a narrative overview before diving into the tiered tracks below.

What You Will Learn

• Foundations — How generative AI and LLMs work, RAG theory, and the LangChain framework

• RAG Optimization — Advanced indexing, hybrid search, re-ranking, GraphRAG, and multimodal retrieval

• Agents — LangGraph workflows, tool calling, multi-agent collaboration, MCP, memory, and production harnesses

• LLMOps — Evaluation metrics, observability, experimentation, AI safety, and end-to-end system integration

• Cheatsheets — Quick-reference pages for prompt patterns, LLM provider APIs, and embeddings

• Exams — Summative theory and project assessments

Prerequisites

None. This track starts from the fundamentals.

• AI Training Overview
• Foundations
• RAG Optimization
• Agents
• LLMOps
• AI Cheatsheets
• Exams